Abstract Alzheimer's disease (AD) as a complex disease currently has limited pathogenic understanding and no cure. However, Genome Wide Association Studies (GWAS) have identified ~203 AD-associated loci based on National Human Genome Research Institute (NHGRI) catalog of published GWAS. These loci include ~3738 single nucleotide polymorphisms (SNPs) in linkage disequilibrium (LD) with r2>0.8. In theory, there is only one functional SNP (fSNP) in each LD that is responsible for the pathogenesis of AD; however, GWAS cannot tell which one is the fSNP in each LD, a challenge in the post-GWAS era. To meet this challenge, we developed two novel techniques: functional Single Nucleotide Polymorphism-next generation sequencing (fSNP-seq) and DNA competition pull-down-Mass spectrometry (DCP-MS). fSNP-seq is a high throughput screen to identify fSNPs with the SNPs in LD on the risk loci revealed by GWAS. DCP-MS uses fSNP sequences as ?bait? to identify their regulatory proteins in a semi-high throughput way with minimum background. We have demonstrated the feasibility of these fSNP-seq and FREP-MS, a prototype of DCP-MS, in our pilot screening on the CD40 locus as well as in a HTP screen on a library that contain 608 juvenile idiopathic arthritis (JIA)- associated SNPs. In this application, we propose two aims to apply our new methods to the GWAS data on AD risk loci. Aim 1. We will use fSNP-seq to identify fSNPs by screening 3738 SNPs on the ~203 AD risk loci and validate the positive hits by allele-imbalanced electrophoretic mobility shift assay (EMSA) and/or CRISPR/Cas9. Aim 2. We will employ DCP-MS to identify the AD risk gene regulators by screening on the validated fSNPs and characterize these proteins by shRNA knockdown and/or CRISPR/Cas9. We will focus our study on the fSNPs on the HLA-DRB5/HLA-DRB1, INPP5D and MEF2C loci that are involved in the inflammation/immune response pathway in AD. The goal of this proposal is to build a foundation for us to study cell type specific AD- associated signal transduction and allele specific transcription network for drug development.